Overall strategy and general description

We aim to develop a multilevel understanding of natural learning that uses complete one-to-one neuron models embedded in full behavioural contexts. By thoroughly understanding the mechanisms of learning in Drosophila larvae, we will derive a more coherent theory that abstracts the essential circuit characteristics. This will be tested by implementation on a robot with multiple sensors able, within a natural environment, to improve its ability to locate a target by learning which sensory gradient(s) are associated with the target, and responding only to the relevant predictive signal. The work has been organised into four main interleaved work packages, each led by one of the participants, with two further work packages for dissemination (WP5) and joint co-ordination (WP6).

WP1 will focus on an in depth understanding of olfactory-gustatory associations, at thebehavioural and neural level. We will use machine vision and machine learning to analyse thelocomotor behaviour of individual larvae to probe the mechanisms underpinning conditionedbehaviours. We will identify the behavioural signatures of learning to reveal the crucial elementsmodified by experience. We will thus go beyond learning theory that predicts only high level changes in behaviour towards a theoretical account of how on-going dynamic search behaviour is modulated in learning, which will be extended to more complex contexts in WP2. In addition we will use optogenetic techniques that allow precise control over activation of sensory and reinforcement pathways, including for the first time simultaneous independent control of two pathways using different wavelengths, as well as multiple neurogenetic methods to probe the output pathways, allowing the precise nature of the learning rule used by larvae in this paradigm to be dissected. The outcome of these investigations will be integrated into and driven by the results of modelling (WP4).

WP2 will explore the breadth of the learning capability of the larvae in theoretically driven behavioural experiments that include second order conditioning, devaluation, operant, multimodal, and learning when US is present as gradient or sparse distribution. These experiments are motivated firstly by conflicting predictions in existing theories of learning, and secondly by the need to evaluate theory in more realistic contexts, needed for the envisioned robot applications in WP4. The experiments will draw on the behavioural analysis methods developed in WP1, and where possible will be further explored using neurogenetic methods so that they can be incorporated in the circuit models developed in WP4. A risk is that some of the paradigms are prohibitively time consuming to perform on the larvae. We will ensure we have a range of planned experiments and run prototype versions to reduce such problems in advance, but it may be necessary to focus on a subset of the most
promising paradigms.

In WP3 we will develop new methods for measuring the activation of identified neurons in a behaving animal. We will first develop a fixed animal preparation to record calcium activity in identified neurons under stimulus the conditions for learning used in WP1. We will then develop methods for recording activity of targeted neurons in freely behaving animal, using a high sensitivity fluorescent imaging system from the base of an inverted microscope coupled to a tracking system to maintain the larva within the fluorescent focus area. Once functional, we can also investigate activation patterns in operant learning and more naturalistic stimulus conditions from WP2. These insights will contribute to the development and validation of modelling (WP4). Obtaining these biofluorescence results is a significant challenge with some risk that good output is not obtained; as a backup contingency we have experience in electrophysiology and bioluminescent measurements as alternatives.

The core of the proposal is WP4, in which we will model the neural circuit at multiple levels and test it in simulated and robot contexts that are designed to closely resemble the paradigms for the larva. Using a single compartment spiking neuron model, we will implement the learning circuit -olfactory receptors, olfactory lobe, Kenyon cell and extrinsic neurons – as currently understood for the larvae. The circuit will be interfaced to a simulated larva that encapsulates current understanding of chemotaxis behaviour in a state-based controller that switches between runs and head-casts. The model will be continuously refined and validated against the data from WP1 and WP3, and extended to encompass the more complex learning situations in WP2. From this we will derive optimised computational learning algorithms and implement them on a robot that solves a real world task, based on the potential application area of precision agriculture. A risk is that no ‘clean’ abstraction of learning rules that can be derived from the larval brain circuits. It might nevertheless be possible to directly exploit the capabilities of the full circuit in artificial systems as the computing power required to represent the entire larval brain is probably within the capacity of today’s standard desktop machines. In this case, to investigate the wider application of the larval learning system, we would interface the robot (wirelessly) directly to the circuit simulation running on a GPU. We would also investigate running the neural simulation on compact dedicated hardware, such as an FPGA or aVLSI circuit. There is also a potential risk that the real-world, real-time sensorimotor contingencies of the robot task raise completely new issues for interpretation of the function of the larval neural circuit. We note that this challenge is one of the motivations for including the robot implementation in the project, and for considering the issues it raises (for example sensor accuracy, temporal resolution, actuation constraints) relatively early during the development of models.

WP5 will focus on dissemination activities including an expansion of the virtual fly brain database to accommodate functional data produced in WPs1-3 and directly generate NeuroML models. It will also act as our internal project data hub for the project as well as a portal for public dissemination. WP6 will focus on administering the overall programme, handling the budgetary and legal issues.